






"""simple docstring"""







import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
  _UpperCamelCase	     =    argparse.ArgumentParser(
      description=(
          """Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
          """ Distillation"""
      )
  )
  parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
  parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
  parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
  parser.add_argument("""--vocab_transform""", action="""store_true""")
  _UpperCamelCase	     =    parser.parse_args()

  if args.model_type == "bert":
    _UpperCamelCase	     =    BertForMaskedLM.from_pretrained(args.model_name)
    _UpperCamelCase	     =    """bert"""
  else:
    raise ValueError("""args.model_type should be \"bert\".""")

  _UpperCamelCase	     =    model.state_dict()
  _UpperCamelCase	     =    {}

  for w in ["word_embeddings", "position_embeddings"]:
    _UpperCamelCase	     =    state_dict[F"""{prefix}.embeddings.{w}.weight"""]
  for w in ["weight", "bias"]:
    _UpperCamelCase	     =    state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]

  _UpperCamelCase	     =    0
  for teacher_idx in [0, 2, 4, 7, 9, 11]:
    for w in ["weight", "bias"]:
      _UpperCamelCase	     =    state_dict[
          F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
      ]
      _UpperCamelCase	     =    state_dict[
          F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
      ]
      _UpperCamelCase	     =    state_dict[
          F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
      ]

      _UpperCamelCase	     =    state_dict[
          F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
      ]
      _UpperCamelCase	     =    state_dict[
          F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
      ]

      _UpperCamelCase	     =    state_dict[
          F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
      ]
      _UpperCamelCase	     =    state_dict[
          F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
      ]
      _UpperCamelCase	     =    state_dict[
          F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
      ]
    std_idx += 1

  _UpperCamelCase	     =    state_dict["""cls.predictions.decoder.weight"""]
  _UpperCamelCase	     =    state_dict["""cls.predictions.bias"""]
  if args.vocab_transform:
    for w in ["weight", "bias"]:
      _UpperCamelCase	     =    state_dict[F"""cls.predictions.transform.dense.{w}"""]
      _UpperCamelCase	     =    state_dict[F"""cls.predictions.transform.LayerNorm.{w}"""]

  print(F"""N layers selected for distillation: {std_idx}""")
  print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

  print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
  torch.save(compressed_sd, args.dump_checkpoint)




"""simple docstring"""


import random





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase = False							)				->   dict:




						'''simple docstring'''



						lowerCamelCase__   ={i: [] for i in range(__lowerCAmelCase							)}

						# if probability is greater or equal than 1, then generate a complete graph
						if probability >= 1:
												return complete_graph(__lowerCAmelCase							)
						# if probability is lower or equal than 0, then return a graph without edges
						if probability <= 0:
												return graph

						# for each couple of nodes, add an edge from u to v
						# if the number randomly generated is greater than probability probability
						for i in range(__lowerCAmelCase							):
												for j in range(i + 1  ,       __lowerCAmelCase							):
																		if random.random() < probability:
																								graph[i].append(__lowerCAmelCase							)
																								if not directed:
																														# if the graph is undirected, add an edge in from j to i, either
																														graph[j].append(__lowerCAmelCase							)
						return graph





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   dict:




						'''simple docstring'''



						return {
						    i: [j for j in range(__lowerCAmelCase							) if i != j] for i in range(__lowerCAmelCase							)
						}


if __name__ == "__main__":
	import doctest

	doctest.testmod()


